PIMT: Physics-Based Interactive Motion Transition for Hybrid Character Animation

Motion transitions, which serve as bridges between two sequences of character animation, play a crucial role in creating long variable animation for real-time 3D interactive applications. In this paper, we present a framework to produce hybrid character animation, which combines motion capture animation and physical simulation animation that seamlessly connects the front and back motion clips. In contrast to previous works using interpolation for transition, our physics-based approach inherently ensures physical validity, and both the transition moment of the source motion clip and the horizontal rotation of the target motion clip can be specified arbitrarily within a certain range, which achieves high responsiveness and wide latitude for user control. The control policy of character can be trained automatically using only the motion capture data that requires transition, and is enhanced by our proposed Self-Behavior Cloning (SBC), an approach to improve the unsupervised reinforcement learning of motion transition. We show that our framework can accomplish the interactive transition tasks from a fully-connected state machine constructed from nine motion clips with high accuracy and naturalness.

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Publication Details

Published
2024-10-26
DOI
https://doi.org/10.1145/3664647.3681582
Citations
2
Primary Topic
Human Motion and Animation
Type
article
Field-Weighted Citation Impact
0.66
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article

PIMT: Physics-Based Interactive Motion Transition for Hybrid Character Animation

Ning Xie, Zheng Li, Wei Zhang, Yongdong Deng
2 citations
Human Motion and Animation
0.66
article

PIMT: Physics-Based Interactive Motion Transition for Hybrid Character Animation

Ning Xie, Zheng Li, Wei Zhang, Yongdong Deng
article en
2 citations

Abstract

Motion transitions, which serve as bridges between two sequences of character animation, play a crucial role in creating long variable animation for real-time 3D interactive applications. In this paper, we present a framework to produce hybrid character animation, which combines motion capture animation and physical simulation animation that seamlessly connects the front and back motion clips. In contrast to previous works using interpolation for transition, our physics-based approach inherently ensures physical validity, and both the transition moment of the source motion clip and the horizontal rotation of the target motion clip can be specified arbitrarily within a certain range, which achieves high responsiveness and wide latitude for user control. The control policy of character can be trained automatically using only the motion capture data that requires transition, and is enhanced by our proposed Self-Behavior Cloning (SBC), an approach to improve the unsupervised reinforcement learning of motion transition. We show that our framework can accomplish the interactive transition tasks from a fully-connected state machine constructed from nine motion clips with high accuracy and naturalness.

University of Electronic Science and Technology of China (CN)
Openalex Percentile: Top 29%
Human Motion and Animation
0.66
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